


'''simple docstring'''







import argparse
import hashlib
import os
import urllib
import warnings

import torch
from torch import nn
from tqdm import tqdm

from transformers import WhisperConfig, WhisperForConditionalGeneration


lowerCAmelCase     :  Any											=    {
    '''tiny.en''': '''https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt''',
    '''tiny''': '''https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt''',
    '''base.en''': '''https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt''',
    '''base''': '''https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt''',
    '''small.en''': '''https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt''',
    '''small''': '''https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt''',
    '''medium.en''': '''https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt''',
    '''medium''': '''https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt''',
    '''large''': '''https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt''',
    '''large-v2''': '''https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt''',
}






def       lowercase					(_A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Optional[Any]       =							['layers', 'blocks']
       for k in ignore_keys:
              state_dict.pop(_A ,    _A       )


lowerCAmelCase     :  Any											=    {
    '''blocks''': '''layers''',
    '''mlp.0''': '''fc1''',
    '''mlp.2''': '''fc2''',
    '''mlp_ln''': '''final_layer_norm''',
    '''.attn.query''': '''.self_attn.q_proj''',
    '''.attn.key''': '''.self_attn.k_proj''',
    '''.attn.value''': '''.self_attn.v_proj''',
    '''.attn_ln''': '''.self_attn_layer_norm''',
    '''.attn.out''': '''.self_attn.out_proj''',
    '''.cross_attn.query''': '''.encoder_attn.q_proj''',
    '''.cross_attn.key''': '''.encoder_attn.k_proj''',
    '''.cross_attn.value''': '''.encoder_attn.v_proj''',
    '''.cross_attn_ln''': '''.encoder_attn_layer_norm''',
    '''.cross_attn.out''': '''.encoder_attn.out_proj''',
    '''decoder.ln.''': '''decoder.layer_norm.''',
    '''encoder.ln.''': '''encoder.layer_norm.''',
    '''token_embedding''': '''embed_tokens''',
    '''encoder.positional_embedding''': '''encoder.embed_positions.weight''',
    '''decoder.positional_embedding''': '''decoder.embed_positions.weight''',
    '''ln_post''': '''layer_norm''',
}






def       lowercase					(_A       ):







       """simple docstring"""







       _lowerCAmelCase     :				List[str]       =							list(s_dict.keys()       )
       for key in keys:
              _lowerCAmelCase     :				Optional[int]       =							key
              for k, v in WHISPER_MAPPING.items():
                     if k in key:
                            _lowerCAmelCase     :				Tuple       =							new_key.replace(_A ,    _A       )

              print(f'{key} -> {new_key}'       )

              _lowerCAmelCase     :				Tuple       =							s_dict.pop(_A       )
       return s_dict






def       lowercase					(_A       ):







       """simple docstring"""







       _lowerCAmelCase      ,  _lowerCAmelCase     :				str       =							emb.weight.shape
       _lowerCAmelCase     :				List[Any]       =							nn.Linear(_A ,    _A ,    bias=_A       )
       _lowerCAmelCase     :				Optional[int]       =							emb.weight.data
       return lin_layer






def       lowercase					(_A ,    _A       ):







       """simple docstring"""







       os.makedirs(_A ,    exist_ok=_A       )
       _lowerCAmelCase     :				str       =							os.path.basename(_A       )

       _lowerCAmelCase     :				Any       =							url.split('/'       )[-2]
       _lowerCAmelCase     :				int       =							os.path.join(_A ,    _A       )

       if os.path.exists(_A       ) and not os.path.isfile(_A       ):
              raise RuntimeError(f'{download_target} exists and is not a regular file'       )

       if os.path.isfile(_A       ):
              _lowerCAmelCase     :				int       =							open(_A ,    'rb'       ).read()
              if hashlib.shaaaa(_A       ).hexdigest() == expected_shaaaa:
                     return model_bytes
              else:
                     warnings.warn(f'{download_target} exists, but the SHA256 checksum does not match; re-downloading the file'       )

       with urllib.request.urlopen(_A       ) as source, open(_A ,    'wb'       ) as output:
              with tqdm(
                  total=int(source.info().get('Content-Length'       )       ) ,    ncols=8_0 ,    unit='iB' ,    unit_scale=_A ,    unit_divisor=1_0_2_4       ) as loop:
                     while True:
                            _lowerCAmelCase     :				Union[str, Any]       =							source.read(8_1_9_2       )
                            if not buffer:
                                   break

                            output.write(_A       )
                            loop.update(len(_A       )       )

       _lowerCAmelCase     :				Union[str, Any]       =							open(_A ,    'rb'       ).read()
       if hashlib.shaaaa(_A       ).hexdigest() != expected_shaaaa:
              raise RuntimeError(
                  'Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.'       )

       return model_bytes






def       lowercase					(_A ,    _A       ):







       """simple docstring"""







       if ".pt" not in checkpoint_path:
              _lowerCAmelCase     :				Optional[Any]       =							_download(_MODELS[checkpoint_path]       )
       else:
              _lowerCAmelCase     :				Optional[int]       =							torch.load(_A ,    map_location='cpu'       )
       _lowerCAmelCase     :				List[str]       =							original_checkpoint['dims']
       _lowerCAmelCase     :				Optional[int]       =							original_checkpoint['model_state_dict']
       _lowerCAmelCase     :				Tuple       =							state_dict['decoder.token_embedding.weight']
       remove_ignore_keys_(_A       )
       rename_keys(_A       )
       _lowerCAmelCase     :				str       =							True
       _lowerCAmelCase     :				str       =							state_dict['decoder.layers.0.fc1.weight'].shape[0]

       _lowerCAmelCase     :				int       =							WhisperConfig(
           vocab_size=dimensions['n_vocab'] ,    encoder_ffn_dim=_A ,    decoder_ffn_dim=_A ,    num_mel_bins=dimensions['n_mels'] ,    d_model=dimensions['n_audio_state'] ,    max_target_positions=dimensions['n_text_ctx'] ,    encoder_layers=dimensions['n_audio_layer'] ,    encoder_attention_heads=dimensions['n_audio_head'] ,    decoder_layers=dimensions['n_text_layer'] ,    decoder_attention_heads=dimensions['n_text_state'] ,    max_source_positions=dimensions['n_audio_ctx'] ,    )

       _lowerCAmelCase     :				List[str]       =							WhisperForConditionalGeneration(_A       )
       _lowerCAmelCase      ,  _lowerCAmelCase     :				Optional[Any]       =							model.model.load_state_dict(_A ,    strict=_A       )
       if len(_A       ) > 0 and not set(_A       ) <= {
           "encoder.embed_positions.weights",
           "decoder.embed_positions.weights",
       }:
              raise ValueError(
                  'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'
                  f' but all the following weights are missing {missing}'       )

       if tie_embeds:
              _lowerCAmelCase     :				Optional[int]       =							make_linear_from_emb(model.model.decoder.embed_tokens       )
       else:
              _lowerCAmelCase     :				Optional[int]       =							proj_out_weights

       model.save_pretrained(_A       )


if __name__ == "__main__":
       lowerCAmelCase     :  Optional[Any]											=    argparse.ArgumentParser()
       # # Required parameters
       parser.add_argument("""--checkpoint_path""", type=str, help="""Patht to the downloaded checkpoints""")
       parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
       lowerCAmelCase     :  Optional[Any]											=    parser.parse_args()

       convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)



"""simple docstring"""



import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


a       :						int       		=		'''scheduler_config.json'''




class 		a_    (			_UpperCAmelCase						):
    a   :					List[Any]			     =					1
    a   :					Tuple			     =					2
    a   :					Dict			     =					3
    a   :					str			     =					4
    a   :					Optional[int]			     =					5
    a   :					Any			     =					6
    a   :					int			     =					7
    a   :					Any			     =					8
    a   :					List[Any]			     =					9
    a   :					Any			     =					10
    a   :					List[str]			     =					11
    a   :					Optional[int]			     =					12
    a   :					Any			     =					13
    a   :					Dict			     =					14







@dataclass
class 		a_    (			_UpperCAmelCase						):
    a   :					torch.FloatTensor







class 		a_    :
    a   :					Dict			     =					SCHEDULER_CONFIG_NAME
    a   :					List[str]			     =					[]
    a   :					Optional[int]			     =					True







    @classmethod
    def   _snake_case       (      cls      :							List[str]  ,       __UpperCamelCase      :							Dict[str, Any] = None  ,       __UpperCamelCase      :							Optional[str] = None  ,       __UpperCamelCase      :							List[str]=False  ,       **__UpperCamelCase      :							int  ,       )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = cls.load_config(
            pretrained_model_name_or_path=__UpperCamelCase  ,       subfolder=__UpperCamelCase  ,       return_unused_kwargs=__UpperCamelCase  ,       return_commit_hash=__UpperCamelCase  ,       **__UpperCamelCase  ,       )
        return cls.from_config(__UpperCamelCase  ,       return_unused_kwargs=__UpperCamelCase  ,       **__UpperCamelCase  )







    def   _snake_case       (      self      :							Tuple  ,       __UpperCamelCase      :							Union[str, os.PathLike]  ,       __UpperCamelCase      :							bool = False  ,       **__UpperCamelCase      :							List[str]  )		->Any:






        '''simple docstring'''






        self.save_config(save_directory=__UpperCamelCase  ,       push_to_hub=__UpperCamelCase  ,       **__UpperCamelCase  )







    @property
    def   _snake_case       (      self      :							Optional[int]  )		->List[Any]:






        '''simple docstring'''






        return self._get_compatibles()







    @classmethod
    def   _snake_case       (      cls      :							List[str]  )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = list(set([cls.__name__] + cls._compatibles  )  )
        _UpperCAmelCase        = importlib.import_module(__name__.split("""."""  )[0]  )
        _UpperCAmelCase        = [
            getattr(__UpperCamelCase  ,       __UpperCamelCase  ) for c in compatible_classes_str if hasattr(__UpperCamelCase  ,       __UpperCamelCase  )
        ]
        return compatible_classes